Reinforcement learning from verifiable rewards (RLVR) has become a promising paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing RL-based training remains only a rough approximation to human learning, where humans leverage external experience to guide exploration and gradually internalize problem-solving strategies into stable knowledge. Motivated by this gap, we ask: how can LLMs better utilize and internalize experience during RLVR training? To answer this question, we propose Dual Guidance Optimization (DGO), which treats experience as a bridge between external guidance and internal policy learning. Rather than using experience only as an inference-time aid or trajectories only as optimization signals, DGO couples them in a closed loop: experience guides exploration, improved trajectories renew the experience bank, and guided trajectories are rewritten as self-contained traces for policy distillation. This enables the model to better utilize external experience while yielding consistent gains under experience-free inference. Experiments across multiple benchmarks show that DGO consistently outperforms baselines under both experience-free and experience-guided inference, suggesting that coupling utilization and internalization leads to effective reasoning.

While large language models (LLMs) have mastered syntax-level code generation, complex algorithmic reasoning remains a challenge, typically addressed by scaling model depth and parameter count. Universal Transformers (UT) offer a compelling alternative by introducing a recurrent inductive bias that aligns with the recursive nature of programming logic. However, training looped architectures at scale has historically been hindered by severe instability and optimization difficulties associated with backpropagation through time (BPTT). We present LoopCoder (40B-A80B) pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants—the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters. Unlike prior approaches that restrict recurrence to small-scale tasks, we implement a comprehensive looped training protocol spanning both pre-training and post-training phases. We initiate the model via dense-to-loop transformation, folding a pre-trained dense checkpoint to initialize a recurrent block, followed by rigorous looped pre-training and specialized post-training for instruction following and reasoning. Our results establish a robust recipe for scaling coding intelligence via recurrent computation, proving that dense checkpoints serve as an optimal foundation for evolving into dynamic, looped reasoners.

Agentic reward models enhance large language model (LLM) evaluation by incorporating external tool-use capabilities. However, their training is heavily constrained by coarse-grained outcome supervision and the high cost of process-level annotations. To address these challenges, we propose SEAR (Self-judging agentic reward models), a novel training framework that enables agentic reward models to self-judge their own judging processes. SEAR introduces a dual-role adversarial co-evolution framework, where the model alternates between a judger and a self-judger within a single generation process: the judger is optimized to produce more rigorous reasoning trajectories that maximize self-judgment scores, while the self-judger learns to provide critical and discriminative evaluations. Moreover, SEAR further enables agentic RMs to self-evolve without relying on external human annotations. Extensive experiments demonstrate that SEAR consistently improves the reliability of agentic reward models across diverse benchmarks.

Industrial code tasks, including chip design, GPU optimization, and firmware, often fail for reasons that are invisible in ordinary source code: timing constraints, backend logs, layout data, simulator behavior, and hardware-specific performance trade-offs. Current code models see many programs but few expert correction traces from these settings. We present InCoder-32B-Thinking, a 32B thinking code model trained with Error-driven Chain-of-Thought (ECoT) synthesis and an Industrial Code World Model (ICWM). ECoT converts several rounds of toolchain signals into reasoning traces by preserving the failed code, the observed failure, and the later fix. ICWM learns from execution traces in domains such as Verilog simulation and GPU profiling, then predicts feedback for the data engine, which can build more repair traces without routing every candidate through a real toolchain. The data keeps the uneven reasoning depth of industrial work: short traces for simple cases, long traces for hardware-level debugging. Across 23 tests (14 general and 9 industrial), InCoder-32B-Thinking reaches 81.3% on LiveCodeBench v5, 84.0% CAD-Coder compile success, and 38.0% on KernelBench L2, while remaining competitive on general coding tasks. Model weights and code are available at Hugging Face: https://huggingface.co/Multilingual-Multimodal-NLP/IndustrialCoder and GitHub: https://github.com/CSJianYang/Industrial-Coder.

Although the Universal Transformer (UT) mitigates the diminishing returns of standard LLM scaling by decoupling parameter count from depth, it remains constrained by linear computational costs and rigid weight-sharing mechanisms. These limitations lead to severe functional homogeneity, which subsequently induces over-smoothing, representation rank collapse, and degraded reasoning performance. In this work, we present the first systematic study of Compute Distribution Skew, identifying it as the primary driver of extrapolation failure. This is a pathological phenomenon in ultra-deep recurrent Transformers characterized by a disproportionate distribution of contributions across recurrent steps, resulting in distinct functional states during prefix and suffix processing phases. To address this challenge, we propose the Polymorphic Transformer, which aims to achieve functional polymorphism and depth sparsity within a shared-parameter framework. By integrating conditional sparse subspaces, SiLU Attention, and an uncertainty-aware depth scheduler, our architecture mitigates power-method collapse and effectively decouples logical depth from computational cost. Experiments demonstrate that our model significantly enhances representation rank and robustness, achieving complex reasoning performance comparable to baseline while reducing computation by 64.7%.